Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation. (July 2016)

• Record Type: Journal Article
• Authors: Verhoog, Reinier; Ghorbani, Amineh; Dijkema, Gerard P.J.
• Is Part Of: Environmental modelling & software. Volume 81(2016:Jul.)
• Page Start: 72
• Page End: 85
• Publication Date: 2016-07
• Subjects: Agent-based model -- Socio-ecological system -- Human-environment system -- Conceptual modelling -- Modelling institutions; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2016.03.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs. Highlights: Regional manure-based biogas production can be feasible in the Netherlands. We used the MAIA modelling framework to build an ABM of a biogas infrastructure. MAIA enables the incorporation of institutional aspects in ABM. MAIA is an integrated solution for addressing modelling challenges in SES. Future research: Automatic code generation; Dynamic feedback of simulation results.